A Fuzzy MCDM Approach for Project Selection Criteria Prioritization in a Big-Four Company: Evidences from Turkey Consultancy Sector

Beldek, Tuğçe; Konyalıoğlu, Aziz Kemal; Akdağ, Hatice Camgöz

doi:10.1007/978-3-030-23756-1_95

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Location selection and assessment under fuzzy settings is more accurate, since it considers more criteria and options. Fuzzy TOPSIS additionally prioritizes country options by minimizing uncontrollable elements and optimizing controllable factors [52,53]. Its straightforward approach, easily programmable computation mechanism, and improved outcomes make it popular.…”

Section: Comparative Analysis Of Mcdm Techniquesmentioning

confidence: 99%

Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry

Aungkulanon,

Atthirawong,

Sangmanee

et al. 2023

Mathematics

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This research paper presents an all-encompassing methodology for multi-criteria decision-making in the apparel sector, with the particular objective of aiding in the determination of the most appropriate location within the CLMV subregion. The research is conducted in three crucial stages. The process began with the administration of a survey to proprietors of garment businesses in both Thailand and the CLMV countries. This survey resulted in the compilation of an exhaustive list of site-selection criteria and sub-criteria. Based on the findings of subject matter-expert interviews, Cambodia (C), Vietnam (V), and Myanmar (M) were identified as feasible alternatives. Subsequently, the questionnaire criteria and sub-criteria were evaluated utilizing the Fuzzy Analytic Network Process (Fuzzy ANP), which involved the utilization of meticulously designed pair-wise comparison matrices and local priorities. Five specialists from the Thai entrepreneurial community affirmed the effectiveness of Fuzzy ANP and expressed interest in expanding manufacturing operations in the CLMV subregion. The optimal location for Thai apparel manufacturers was subsequently determined using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The results indicated that Vietnam is the most favorable option. In order to improve the dependability of results, an amended mixture-design scenario analysis was implemented. This analysis assessed the sensitivity and dependability of the proposed model in different scenarios, ensuring its applicability in real-world situations. In contrast to traditional models, this study integrates managerial judgments and preferences into the decision-making procedure, thereby accounting for the complex interdependencies among numerous criteria. The suggested methodology functions as a beneficial instrument for decision-makers, both domestic and international, as it integrates effortlessly into the organizational structure of the CLMV region. By harmonizing objectives pertaining to data acquisition, manipulation, retention, and dissemination, this framework not only enables enhanced decision-making processes, but also optimizes system efficiency.

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Section: Comparative Analysis Of Mcdm Techniquesmentioning

confidence: 99%

Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry

Aungkulanon,

Atthirawong,

Sangmanee

et al. 2023

Mathematics

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“…Moreover, the factors were collected for a specific region or industry, which may not generalize the study results to the wider audience. Furthermore, the identified factors were analyzed using various MCDM and not with fuzzy TOPSIS method which offers comparatively better results (Singh et al , 2018; Gupta et al , 2019; Beldek et al , 2020). Considering mentioned limitations, this study identified the project complexity factors through both literature survey and experts’ opinions, which authenticate the study outcomes to the wider audience.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is not an easy task to decide a project based on minimizing uncontrollable factors and maximizing controllable factors. In this MCDM problem, the fuzzy approach can be a useful guideline to evaluate project selection (Gupta et al , 2019; Beldek et al , 2020). Fuzzy TOPSIS is also a multi-criteria technique that has been widely used due to its clear methodology and easily programmable computation procedure (Singh et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…To find out the complex projects, one solution is to look for the attributes or factors, which are responsible for making any project complex. These factors are then can be analyzed with support from the MCDM method and compare the identified factors responsible for the project’s complexity of an organization (Beldek et al , 2020). However, in real life, the evaluation and selection of complex projects depend on various factors, which are mostly subjective in nature.…”

Section: Introductionmentioning

confidence: 99%

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Application of fuzzy TOPSIS framework for selecting complex project in a case company

Shamsuzzoha

Piya

Shamsuzzaman

2021

JGOSS

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Purpose This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions. Design/methodology/approach The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations. Findings A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works. Research limitations/implications The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity. Originality/value The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

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Project Selection Under U-Number-Valued Information

Jabbarova

2022

11th International Conference on Theory and Application of Soft Computing, Computing With Words and Perceptions and Artificial

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A Fuzzy MCDM Approach for Project Selection Criteria Prioritization in a Big-Four Company: Evidences from Turkey Consultancy Sector

Cited by 4 publications

References 15 publications

Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry

Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry

Application of fuzzy TOPSIS framework for selecting complex project in a case company

Project Selection Under U-Number-Valued Information

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